Analysis

Preparations

Loading the neccessary packages

library("tidyverse")
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 2.2.1     ✔ purrr   0.2.4
✔ tibble  1.4.2     ✔ dplyr   0.7.4
✔ tidyr   0.7.2     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library("Seurat")
Loading required package: cowplot

Attaching package: ‘cowplot’

The following object is masked from ‘package:ggplot2’:

    ggsave

Loading required package: Matrix

Attaching package: ‘Matrix’

The following object is masked from ‘package:tidyr’:

    expand
library("Matrix")
library("stringr")
library("knitr")
library("kableExtra")
library("pheatmap")
library("RColorBrewer")
library("clusterProfiler")
Loading required package: DOSE
DOSE v3.4.0  For help: https://guangchuangyu.github.io/DOSE

If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609

clusterProfiler v3.4.4  For help: https://guangchuangyu.github.io/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu., Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

Attaching package: ‘clusterProfiler’

The following object is masked from ‘package:purrr’:

    simplify
library("ReactomePA")
ReactomePA v1.22.0  For help: https://guangchuangyu.github.io/ReactomePA

If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479
save_csv <- TRUE

Setup vectors needed for the analysis: Genes which are markers for the cell types

markers.biol.validated <- c("Thbs4","Cxcl14","Cd9","Nr2e1","Id2","Ascl1","Egfr","Dcx","Dlx1","Mki67","Sox2","S100b","Cd24a","Ift88","Foxj1","Pdgfrb","Pecam1","Slc1a3","Gfap","Nes","Cxcl10","Rpl32","Hes1","Hes5")
markers.custom.order <- c("Sfrp5","Bmpr1a","Vcam1","Slc1a3","Id2","Hes1","Hes5","Egfr","Ascl1","Mki67","Rpl22","Cd9","Nr2e1","Sox2","S100b")

Load a list of cell cycle specific genes (taken from: http://satijalab.org/seurat/cell_cycle_vignette.html)

cc.genes <- readLines(con = "cell_cycle_genes/cell_cycle_vignette_files/regev_lab_cell_cycle_genes.txt")
cc.genes <- str_to_title(cc.genes)
# Separate markers of G2/M phase and markers of S phase
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]

Loading the data

datadir <- "count_table/filtered_gene_bc_matrices_mex/mm10/"
data_10X2 <- Read10X(data.dir = file.path( datadir) )

Create a Seurat object with the 10X data

seurat_10X2 <- CreateSeuratObject(raw.data = data_10X2, min.cells = 3, min.genes = 1500, project = "young_vs_old_10X2")

Add Metadata and check QC values

mito.genes <- grep(pattern = "^mt-", x = rownames(x = seurat_10X2@data), value = TRUE)
percent.mito <- Matrix::colSums(seurat_10X2@raw.data[mito.genes, ])/Matrix::colSums(seurat_10X2@raw.data)
seurat_10X2 <- AddMetaData(object = seurat_10X2, metadata = percent.mito, col.name = "percent.mito")
VlnPlot(object = seurat_10X2, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3 )

Annotate the cells with the age of the animals

We can also annotate the age of the mouse from which the cells were prepared (young or old). We know that all cell barcodes ending in ...-1 are beloning to cells from old old mice, the cell barcodes ending in ...-2 are from young mice.

age.cells <- data.frame( age = as.factor(rownames(seurat_10X2@meta.data)) ) 
rownames(age.cells) <- rownames(seurat_10X2@meta.data) 

age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-1$" , replacement = "old")
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-2$" , replacement = "young")

age.cells$age_num <- age.cells$age
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "old" , replacement = "1")
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "young" , replacement = "2")
age.cells$age_num <- as.numeric(age.cells$age_num)

age.cells$age <- factor(age.cells$age)

seurat_10X2 <- AddMetaData(seurat_10X2, age.cells , "age")

Check scatterplots of nUMI, nGene and % of mitochondrial genes

par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")

Filter the cells which have too high mitochondrial content

seurat_10X2 <- FilterCells(object = seurat_10X2, subset.names = c("nGene", "percent.mito"), 
    low.thresholds = c(1500, -Inf), high.thresholds = c(4500, 0.10))

Check scatterplots of nUMI, nGene and % of mitochondrial genes after filtering

par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")

Log Normalize the data

seurat_10X2 <- NormalizeData(object = seurat_10X2, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

Scale the data and regress out the influence of nUMI and percent.mito

seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = c("nUMI", "percent.mito" , "nGene"))

Regress out difference between S phase and G2/M Phase

seurat_10X2 <- CellCycleScoring(object = seurat_10X2, s.genes = s.genes, g2m.genes = g2m.genes, set.ident = TRUE)

Meta data columns

We can see that the columns S.Score, G2M.Score and Phase are now added to the meta data table

head(seurat_10X2@meta.data)
seurat_10X2@meta.data$CC.Difference <- seurat_10X2@meta.data$S.Score - seurat_10X2@meta.data$G2M.Score
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = "CC.Difference", display.progress = FALSE)

Dimensional reduction and clustering

Find variable genes

seurat_10X2 <- FindVariableGenes(object = seurat_10X2, mean.function = ExpMean, dispersion.function = LogVMR, 
    x.low.cutoff = 0.0125, x.high.cutoff = 4, y.cutoff = 0.5)
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

Using these parameters we have identified 2236 genes as variable.

Run PCA

Now we use the detected variable genes to peform PCA on the data

seurat_10X2 <- RunPCA(object = seurat_10X2, pc.genes = seurat_10X2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5  , seed.use = 2 )
seurat_10X2 <- ProjectPCA(object = seurat_10X2 )
seurat_10X2 <- JackStraw(object = seurat_10X2  )

Elbowplot PCA

PCElbowPlot(object = seurat_10X2)

PCA Plot

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)

Run t-SNE

Use graph-based clustering to cluster the cells in our dataset

seurat_10X2 <- FindClusters(seurat_10X2, reduction.type = "pca", dims.use = 1:8, save.SNN = T , force.recalc = TRUE)

Plot t-SNE plot

TSNEPlot(object = seurat_10X2)

FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("red","forestgreen") , no.legend = FALSE ) 

FeaturePlot(object = seurat_10X2 , features.plot = "nUMI" , cols.use = c("lightgrey","red") , no.legend = FALSE )

FeaturePlot(object = seurat_10X2 , features.plot = "nGene" , cols.use = c("lightgrey","red") , no.legend = FALSE )

Let's look at the

markers.seurat_10X2 <- FindAllMarkers(seurat_10X2, print.bar = FALSE , only.pos = TRUE , return.thresh = 0.05)
markers.seurat_10X2 %>% filter(cluster == 0)
markers.seurat_10X2 %>% filter(cluster == 1)
markers.seurat_10X2 %>% filter(cluster == 2)
markers.seurat_10X2 %>% filter(cluster == 3)
markers.seurat_10X2 %>% filter(cluster == 4)
markers.seurat_10X2 %>% filter(cluster == 5)
markers.seurat_10X2 %>% filter(cluster == 6)
markers.seurat_10X2 %>% filter(cluster == 7)
markers.seurat_10X2 %>% filter(cluster == 8)
markers.seurat_10X2 %>% filter(cluster == 9)
markers.seurat_10X2 %>% filter(cluster == 10)

Differential gene expression between cluster 6 and 8

markers_cluster_6_vs_8 <- FindMarkers(object = seurat_10X2 , ident.1 = 6 , ident.2 = 8 , only.pos = TRUE , print.bar = FALSE )
markers_cluster_6_vs_8

Cluster 6 shows increased expression of Mapk-Pathway related genes, immediate-early genes and transcription factors, like: Jun, Fos, Atf3, Ier2 ...

markers_cluster_8_vs_6 <- FindMarkers(object = seurat_10X2 , ident.1 = 8  , ident.2 = 6 , only.pos = TRUE , print.bar = FALSE )
markers_cluster_8_vs_6

tSNE feature plots: marker genes

TSNEPlot(object = seurat_10X2)

## quiescent Markers
FeaturePlot(object = seurat_10X2 , features.plot = "Thbs4" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Id2" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl14" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## Stem Cell markers
FeaturePlot(object = seurat_10X2 , features.plot = "Slc1a3" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Nr2e1" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Hes1" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Hes5" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Nes" ,cols.use = c("lightblue","red") , no.legend = FALSE)

##
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl10" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## Proliferation markers
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl22" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Rpl32" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Cd9" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## Astrocyte markers
FeaturePlot(object = seurat_10X2 , features.plot = "S100b" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Gfap" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## Active NSC markers
FeaturePlot(object = seurat_10X2 , features.plot = "Mki67" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Ascl1" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Egfr" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## TAPs
FeaturePlot(object = seurat_10X2 , features.plot = "Vcam1" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Dlx1" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## Neuroblast marker
FeaturePlot(object = seurat_10X2 , features.plot = "Dcx" ,cols.use = c("lightblue","red") , no.legend = FALSE)

##
FeaturePlot(object = seurat_10X2 , features.plot = "Sfrp5" ,cols.use = c("lightblue","red") , no.legend = FALSE)

## Oligodenrocyte marker
FeaturePlot(object = seurat_10X2 , features.plot = "Olig1" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Pdgfra" ,cols.use = c("lightblue","red") , no.legend = FALSE)

FeaturePlot(object = seurat_10X2 , features.plot = "Sox10" ,cols.use = c("lightblue","red") , no.legend = FALSE)

Check tSNE Plot with markers found by Basak et al. (2018)

# Load the genes as vector
markers <- c( "Agt","Slc6a9","Etnppl","Slc6a1","Sparc",
                            "Slc1a3","Bcan","Tspan7","Htra1","Cldn10","Ptn","Acsl6","Fgfr3",
                            "Sparcl1","Atp1a2","Gpr37l1","Gja1","Prnp","Acsl3","Aqp4","Apoe","Gm26917",       # markers for quiescent NSCs - Gm26917 = Rn45s
                            "Cst3","Clu","Slc1a2","Prdx6","Mt1","Aldoc",                                    # shared between quiescent and primed NSCs - slc1a2 instead of scl1a2 (probably typo)
                            "Thbs4","Ntrk2","Fxyd1","Gstm1","Igfbp5","S100a6","Itm2b","Sfrp1","Dkk3","C4b",
                            "Acot1","Luc7l3","Ckb",
                            "Cpe","Dbi",                                                                    # primed NSCs and early active NSCs
                            "Miat","Lima1","Pabpc1","Ascl1","Rpl12","Mycn","Olig2",
                            "Pcna","Hsp90aa1","Hnrnpab","Ran","Ppia",
                            "Eef1a1","Ptma","Rpl41","Npm1", "Rpsa", "Fabp7", "Egfr",                        # active NSCs
                            "Mki67","Dlx2","Dlx1","Cdca3","Dlx1as",
                            "Nrep","Tubb2b","Dcx","Btg1","Nfib",
                            "Gad1","Ndrg4","Snap25","Syt1","Rbfox3",
                            "Tmsb10","Stmn2","Cd24a","Dlx6os1","Tubb5","Tubb3","Ccnd2","Hmgn2","H2afz","Sox11","Tuba1b","Tmsb4x","Stmn1","Tpt1","Rpl18a"
                            )  
interval <- c( seq( 1, length(markers) , by = round(length(markers)/4) ) , length(markers) )
interval
[1]  1 24 47 70 92

a) Ascl6 - Clu

TSNEPlot(object = seurat_10X2)

for(i in seq( interval[1] , interval[2] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}

b) Sfrp1 - Ascl1

TSNEPlot(object = seurat_10X2)

for(i in seq( interval[2]+1 , interval[3] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}

c) Npm1 - Dcx

TSNEPlot(object = seurat_10X2)

for(i in seq( interval[3]+1 , interval[4] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}

d) Tubb5 - Rpl18a

TSNEPlot(object = seurat_10X2)

for(i in seq( interval[4]+1 , interval[5] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}

Violin plots: marker genes

## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" )

VlnPlot(object = seurat_10X2 , features.plot = "Id2" )

## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" )

VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" )

VlnPlot(object = seurat_10X2 , features.plot = "Hes1" )

VlnPlot(object = seurat_10X2 , features.plot = "Hes5" )

VlnPlot(object = seurat_10X2 , features.plot = "Nes" )

##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" )

VlnPlot(object = seurat_10X2 , features.plot = "Cxcl14" )

## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" )

VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" )

VlnPlot(object = seurat_10X2 , features.plot = "Cd9" )

## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" )

VlnPlot(object = seurat_10X2 , features.plot = "Gfap" )

## Active NSC markers & TAPS
VlnPlot(object = seurat_10X2 , features.plot = "Mki67" )

VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" )

VlnPlot(object = seurat_10X2 , features.plot = "Egfr" )

VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" )

VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" )

VlnPlot(object = seurat_10X2 , features.plot = "Dlx2" )

VlnPlot(object = seurat_10X2 , features.plot = "Atp1a2" )

## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" )

##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" )

## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" )

VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" )

VlnPlot(object = seurat_10X2 , features.plot = "Sox10" )

Heatmap marker genes

Set identities from marker gene expression

seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(0) ) , ident.use = "qNSC1"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(4) ) , ident.use = "qNSC2"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(6,8) ) , ident.use = "aNSC0"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(3,5) ) , ident.use = "aNSC1"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(1) ) , ident.use = "aNSC2"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(2) ) , ident.use = "TAP"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(7) ) , ident.use = "NB"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(10) ) , ident.use = "OPC"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(9) ) , ident.use = "OD"  )

Build Cluster Tree

seurat_10X2 <- BuildClusterTree(object = seurat_10X2 , pcs.use = 1:8)
[1] "Finished averaging RNA for cluster aNSC0"
[1] "Finished averaging RNA for cluster aNSC1"
[1] "Finished averaging RNA for cluster aNSC2"
[1] "Finished averaging RNA for cluster NB"
[1] "Finished averaging RNA for cluster OD"
[1] "Finished averaging RNA for cluster OPC"
[1] "Finished averaging RNA for cluster qNSC1"
[1] "Finished averaging RNA for cluster qNSC2"
[1] "Finished averaging RNA for cluster TAP"

PCA Plot with identities

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)

PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)

tSNE feature plots: marker genes

TSNEPlot(object = seurat_10X2)

TSNEPlot(object = seurat_10X2 , do.label = TRUE)

Violin plots: marker genes

celltypes_order <- c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD")
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Id2" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Hes1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Hes5" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Nes" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Cd9" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Gfap" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## Active NSC markers
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Egfr" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## TAPs
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = "Sox10" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = c("nUMI")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = c("nGene")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

VlnPlot(object = seurat_10X2 , features.plot = c("percent.mito")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )

Heatmap marker genes

markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )
DoHeatmap(object = seurat_10X2 , genes.use = markers_plots , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE  )

DoHeatmap(object = seurat_10X2 , genes.use = markers.custom.order , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )

DoHeatmap(object = seurat_10X2 , genes.use = markers , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE  )

Save identities to meta.data table

seurat_10X2 <- StashIdent(object = seurat_10X2 , save.name = "celltype") 

Find marker genes for the subpopulations and save them as csv files

markers.seurat_10X2 <- FindAllMarkers(seurat_10X2)

   |+                                                 | 1 % ~26s          
   |++                                                | 2 % ~27s          
   |++                                                | 3 % ~27s          
   |+++                                               | 4 % ~27s          
   |+++                                               | 5 % ~26s          
   |++++                                              | 7 % ~26s          
   |++++                                              | 8 % ~26s          
   |+++++                                             | 9 % ~25s          
   |+++++                                             | 10% ~25s          
   |++++++                                            | 11% ~24s          
   |+++++++                                           | 12% ~24s          
   |+++++++                                           | 13% ~23s          
   |++++++++                                          | 14% ~23s          
   |++++++++                                          | 15% ~23s          
   |+++++++++                                         | 16% ~22s          
   |+++++++++                                         | 18% ~22s          
   |++++++++++                                        | 19% ~22s          
   |++++++++++                                        | 20% ~21s          
   |+++++++++++                                       | 21% ~21s          
   |+++++++++++                                       | 22% ~21s          
   |++++++++++++                                      | 23% ~20s          
   |+++++++++++++                                     | 24% ~20s          
   |+++++++++++++                                     | 25% ~20s          
   |++++++++++++++                                    | 26% ~19s          
   |++++++++++++++                                    | 27% ~19s          
   |+++++++++++++++                                   | 29% ~19s          
   |+++++++++++++++                                   | 30% ~18s          
   |++++++++++++++++                                  | 31% ~18s          
   |++++++++++++++++                                  | 32% ~18s          
   |+++++++++++++++++                                 | 33% ~18s          
   |++++++++++++++++++                                | 34% ~17s          
   |++++++++++++++++++                                | 35% ~17s          
   |+++++++++++++++++++                               | 36% ~17s          
   |+++++++++++++++++++                               | 37% ~17s          
   |++++++++++++++++++++                              | 38% ~16s          
   |++++++++++++++++++++                              | 40% ~16s          
   |+++++++++++++++++++++                             | 41% ~16s          
   |+++++++++++++++++++++                             | 42% ~15s          
   |++++++++++++++++++++++                            | 43% ~15s          
   |++++++++++++++++++++++                            | 44% ~15s          
   |+++++++++++++++++++++++                           | 45% ~14s          
   |++++++++++++++++++++++++                          | 46% ~14s          
   |++++++++++++++++++++++++                          | 47% ~14s          
   |+++++++++++++++++++++++++                         | 48% ~14s          
   |+++++++++++++++++++++++++                         | 49% ~13s          
   |++++++++++++++++++++++++++                        | 51% ~13s          
   |++++++++++++++++++++++++++                        | 52% ~13s          
   |+++++++++++++++++++++++++++                       | 53% ~12s          
   |+++++++++++++++++++++++++++                       | 54% ~12s          
   |++++++++++++++++++++++++++++                      | 55% ~12s          
   |+++++++++++++++++++++++++++++                     | 56% ~11s          
   |+++++++++++++++++++++++++++++                     | 57% ~11s          
   |++++++++++++++++++++++++++++++                    | 58% ~11s          
   |++++++++++++++++++++++++++++++                    | 59% ~11s          
   |+++++++++++++++++++++++++++++++                   | 60% ~10s          
   |+++++++++++++++++++++++++++++++                   | 62% ~10s          
   |++++++++++++++++++++++++++++++++                  | 63% ~10s          
   |++++++++++++++++++++++++++++++++                  | 64% ~09s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~09s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~09s          
   |++++++++++++++++++++++++++++++++++                | 67% ~09s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~08s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~08s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~08s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~07s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~07s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~07s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~07s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~06s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~06s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~06s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 26s

   |+                                                 | 1 % ~40s          
   |++                                                | 2 % ~39s          
   |++                                                | 3 % ~38s          
   |+++                                               | 4 % ~38s          
   |+++                                               | 5 % ~38s          
   |++++                                              | 6 % ~37s          
   |++++                                              | 7 % ~36s          
   |+++++                                             | 8 % ~36s          
   |+++++                                             | 9 % ~36s          
   |++++++                                            | 10% ~35s          
   |++++++                                            | 11% ~35s          
   |+++++++                                           | 12% ~35s          
   |+++++++                                           | 13% ~34s          
   |++++++++                                          | 14% ~34s          
   |++++++++                                          | 15% ~33s          
   |+++++++++                                         | 16% ~33s          
   |+++++++++                                         | 17% ~33s          
   |++++++++++                                        | 18% ~32s          
   |++++++++++                                        | 19% ~32s          
   |+++++++++++                                       | 20% ~31s          
   |+++++++++++                                       | 21% ~31s          
   |++++++++++++                                      | 22% ~31s          
   |++++++++++++                                      | 23% ~30s          
   |+++++++++++++                                     | 24% ~30s          
   |+++++++++++++                                     | 25% ~30s          
   |++++++++++++++                                    | 26% ~29s          
   |++++++++++++++                                    | 27% ~29s          
   |+++++++++++++++                                   | 28% ~28s          
   |+++++++++++++++                                   | 29% ~28s          
   |++++++++++++++++                                  | 30% ~28s          
   |++++++++++++++++                                  | 31% ~27s          
   |+++++++++++++++++                                 | 32% ~27s          
   |+++++++++++++++++                                 | 33% ~26s          
   |++++++++++++++++++                                | 34% ~26s          
   |++++++++++++++++++                                | 35% ~26s          
   |+++++++++++++++++++                               | 36% ~25s          
   |+++++++++++++++++++                               | 37% ~25s          
   |++++++++++++++++++++                              | 38% ~24s          
   |++++++++++++++++++++                              | 39% ~24s          
   |+++++++++++++++++++++                             | 40% ~24s          
   |+++++++++++++++++++++                             | 41% ~23s          
   |++++++++++++++++++++++                            | 42% ~23s          
   |++++++++++++++++++++++                            | 43% ~22s          
   |+++++++++++++++++++++++                           | 44% ~22s          
   |+++++++++++++++++++++++                           | 45% ~22s          
   |++++++++++++++++++++++++                          | 46% ~21s          
   |++++++++++++++++++++++++                          | 47% ~21s          
   |+++++++++++++++++++++++++                         | 48% ~20s          
   |+++++++++++++++++++++++++                         | 49% ~20s          
   |++++++++++++++++++++++++++                        | 51% ~20s          
   |++++++++++++++++++++++++++                        | 52% ~19s          
   |+++++++++++++++++++++++++++                       | 53% ~19s          
   |+++++++++++++++++++++++++++                       | 54% ~18s          
   |++++++++++++++++++++++++++++                      | 55% ~18s          
   |++++++++++++++++++++++++++++                      | 56% ~18s          
   |+++++++++++++++++++++++++++++                     | 57% ~17s          
   |+++++++++++++++++++++++++++++                     | 58% ~17s          
   |++++++++++++++++++++++++++++++                    | 59% ~16s          
   |++++++++++++++++++++++++++++++                    | 60% ~16s          
   |+++++++++++++++++++++++++++++++                   | 61% ~16s          
   |+++++++++++++++++++++++++++++++                   | 62% ~15s          
   |++++++++++++++++++++++++++++++++                  | 63% ~15s          
   |++++++++++++++++++++++++++++++++                  | 64% ~14s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~14s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~14s          
   |++++++++++++++++++++++++++++++++++                | 67% ~13s          
   |++++++++++++++++++++++++++++++++++                | 68% ~13s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~12s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~12s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~12s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~11s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~11s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~10s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~10s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~10s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~09s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~09s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~08s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 39s

   |+                                                 | 1 % ~60s          
   |++                                                | 2 % ~59s          
   |++                                                | 3 % ~59s          
   |+++                                               | 4 % ~59s          
   |+++                                               | 5 % ~58s          
   |++++                                              | 6 % ~57s          
   |++++                                              | 7 % ~56s          
   |+++++                                             | 8 % ~55s          
   |+++++                                             | 9 % ~55s          
   |++++++                                            | 10% ~54s          
   |++++++                                            | 11% ~54s          
   |+++++++                                           | 12% ~53s          
   |+++++++                                           | 13% ~52s          
   |++++++++                                          | 14% ~52s          
   |++++++++                                          | 15% ~52s          
   |+++++++++                                         | 16% ~51s          
   |+++++++++                                         | 18% ~50s          
   |++++++++++                                        | 19% ~50s          
   |++++++++++                                        | 20% ~49s          
   |+++++++++++                                       | 21% ~48s          
   |+++++++++++                                       | 22% ~48s          
   |++++++++++++                                      | 23% ~47s          
   |++++++++++++                                      | 24% ~46s          
   |+++++++++++++                                     | 25% ~46s          
   |+++++++++++++                                     | 26% ~45s          
   |++++++++++++++                                    | 27% ~44s          
   |++++++++++++++                                    | 28% ~44s          
   |+++++++++++++++                                   | 29% ~43s          
   |+++++++++++++++                                   | 30% ~43s          
   |++++++++++++++++                                  | 31% ~42s          
   |++++++++++++++++                                  | 32% ~41s          
   |+++++++++++++++++                                 | 33% ~41s          
   |++++++++++++++++++                                | 34% ~40s          
   |++++++++++++++++++                                | 35% ~39s          
   |+++++++++++++++++++                               | 36% ~39s          
   |+++++++++++++++++++                               | 37% ~38s          
   |++++++++++++++++++++                              | 38% ~37s          
   |++++++++++++++++++++                              | 39% ~37s          
   |+++++++++++++++++++++                             | 40% ~36s          
   |+++++++++++++++++++++                             | 41% ~36s          
   |++++++++++++++++++++++                            | 42% ~35s          
   |++++++++++++++++++++++                            | 43% ~35s          
   |+++++++++++++++++++++++                           | 44% ~34s          
   |+++++++++++++++++++++++                           | 45% ~33s          
   |++++++++++++++++++++++++                          | 46% ~33s          
   |++++++++++++++++++++++++                          | 47% ~32s          
   |+++++++++++++++++++++++++                         | 48% ~32s          
   |+++++++++++++++++++++++++                         | 49% ~31s          
   |++++++++++++++++++++++++++                        | 51% ~30s          
   |++++++++++++++++++++++++++                        | 52% ~30s          
   |+++++++++++++++++++++++++++                       | 53% ~29s          
   |+++++++++++++++++++++++++++                       | 54% ~28s          
   |++++++++++++++++++++++++++++                      | 55% ~28s          
   |++++++++++++++++++++++++++++                      | 56% ~27s          
   |+++++++++++++++++++++++++++++                     | 57% ~27s          
   |+++++++++++++++++++++++++++++                     | 58% ~26s          
   |++++++++++++++++++++++++++++++                    | 59% ~25s          
   |++++++++++++++++++++++++++++++                    | 60% ~25s          
   |+++++++++++++++++++++++++++++++                   | 61% ~24s          
   |+++++++++++++++++++++++++++++++                   | 62% ~23s          
   |++++++++++++++++++++++++++++++++                  | 63% ~23s          
   |++++++++++++++++++++++++++++++++                  | 64% ~22s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~21s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~21s          
   |++++++++++++++++++++++++++++++++++                | 67% ~20s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~20s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~19s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~18s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~18s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~17s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~16s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~16s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~15s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~14s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~14s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~13s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 01s

   |+                                                 | 1 % ~01m 22s      
   |++                                                | 2 % ~01m 22s      
   |++                                                | 3 % ~01m 20s      
   |+++                                               | 4 % ~01m 19s      
   |+++                                               | 5 % ~01m 18s      
   |++++                                              | 6 % ~01m 18s      
   |++++                                              | 7 % ~01m 17s      
   |+++++                                             | 8 % ~01m 16s      
   |+++++                                             | 9 % ~01m 15s      
   |++++++                                            | 10% ~01m 14s      
   |++++++                                            | 11% ~01m 14s      
   |+++++++                                           | 12% ~01m 13s      
   |+++++++                                           | 13% ~01m 13s      
   |++++++++                                          | 14% ~01m 12s      
   |++++++++                                          | 15% ~01m 12s      
   |+++++++++                                         | 16% ~01m 11s      
   |+++++++++                                         | 17% ~01m 10s      
   |++++++++++                                        | 18% ~01m 09s      
   |++++++++++                                        | 19% ~01m 08s      
   |+++++++++++                                       | 20% ~01m 08s      
   |+++++++++++                                       | 21% ~01m 07s      
   |++++++++++++                                      | 22% ~01m 06s      
   |++++++++++++                                      | 23% ~01m 05s      
   |+++++++++++++                                     | 24% ~01m 04s      
   |+++++++++++++                                     | 26% ~01m 03s      
   |++++++++++++++                                    | 27% ~01m 02s      
   |++++++++++++++                                    | 28% ~01m 02s      
   |+++++++++++++++                                   | 29% ~01m 01s      
   |+++++++++++++++                                   | 30% ~60s          
   |++++++++++++++++                                  | 31% ~59s          
   |++++++++++++++++                                  | 32% ~58s          
   |+++++++++++++++++                                 | 33% ~57s          
   |+++++++++++++++++                                 | 34% ~56s          
   |++++++++++++++++++                                | 35% ~55s          
   |++++++++++++++++++                                | 36% ~55s          
   |+++++++++++++++++++                               | 37% ~54s          
   |+++++++++++++++++++                               | 38% ~53s          
   |++++++++++++++++++++                              | 39% ~52s          
   |++++++++++++++++++++                              | 40% ~51s          
   |+++++++++++++++++++++                             | 41% ~50s          
   |+++++++++++++++++++++                             | 42% ~50s          
   |++++++++++++++++++++++                            | 43% ~49s          
   |++++++++++++++++++++++                            | 44% ~48s          
   |+++++++++++++++++++++++                           | 45% ~47s          
   |+++++++++++++++++++++++                           | 46% ~46s          
   |++++++++++++++++++++++++                          | 47% ~45s          
   |++++++++++++++++++++++++                          | 48% ~44s          
   |+++++++++++++++++++++++++                         | 49% ~43s          
   |+++++++++++++++++++++++++                         | 50% ~43s          
   |++++++++++++++++++++++++++                        | 51% ~42s          
   |+++++++++++++++++++++++++++                       | 52% ~41s          
   |+++++++++++++++++++++++++++                       | 53% ~40s          
   |++++++++++++++++++++++++++++                      | 54% ~39s          
   |++++++++++++++++++++++++++++                      | 55% ~38s          
   |+++++++++++++++++++++++++++++                     | 56% ~37s          
   |+++++++++++++++++++++++++++++                     | 57% ~37s          
   |++++++++++++++++++++++++++++++                    | 58% ~36s          
   |++++++++++++++++++++++++++++++                    | 59% ~35s          
   |+++++++++++++++++++++++++++++++                   | 60% ~34s          
   |+++++++++++++++++++++++++++++++                   | 61% ~33s          
   |++++++++++++++++++++++++++++++++                  | 62% ~32s          
   |++++++++++++++++++++++++++++++++                  | 63% ~31s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~31s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~30s          
   |++++++++++++++++++++++++++++++++++                | 66% ~29s          
   |++++++++++++++++++++++++++++++++++                | 67% ~28s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~27s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~26s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~25s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~25s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~24s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~23s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~22s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~21s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~20s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~19s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~18s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 26s

   |+                                                 | 1 % ~02m 30s      
   |++                                                | 2 % ~02m 29s      
   |++                                                | 3 % ~02m 27s      
   |+++                                               | 4 % ~02m 27s      
   |+++                                               | 5 % ~02m 25s      
   |++++                                              | 6 % ~02m 23s      
   |++++                                              | 7 % ~02m 21s      
   |+++++                                             | 8 % ~02m 20s      
   |+++++                                             | 9 % ~02m 18s      
   |++++++                                            | 10% ~02m 17s      
   |++++++                                            | 11% ~02m 15s      
   |+++++++                                           | 12% ~02m 14s      
   |+++++++                                           | 13% ~02m 12s      
   |++++++++                                          | 14% ~02m 12s      
   |++++++++                                          | 15% ~02m 10s      
   |+++++++++                                         | 16% ~02m 08s      
   |+++++++++                                         | 17% ~02m 07s      
   |++++++++++                                        | 18% ~02m 05s      
   |++++++++++                                        | 19% ~02m 04s      
   |+++++++++++                                       | 20% ~02m 02s      
   |+++++++++++                                       | 21% ~02m 01s      
   |++++++++++++                                      | 22% ~01m 59s      
   |++++++++++++                                      | 23% ~01m 58s      
   |+++++++++++++                                     | 24% ~01m 56s      
   |+++++++++++++                                     | 25% ~01m 55s      
   |++++++++++++++                                    | 26% ~01m 53s      
   |++++++++++++++                                    | 27% ~01m 52s      
   |+++++++++++++++                                   | 28% ~01m 50s      
   |+++++++++++++++                                   | 29% ~01m 49s      
   |++++++++++++++++                                  | 30% ~01m 47s      
   |++++++++++++++++                                  | 31% ~01m 45s      
   |+++++++++++++++++                                 | 32% ~01m 44s      
   |+++++++++++++++++                                 | 33% ~01m 43s      
   |++++++++++++++++++                                | 34% ~01m 41s      
   |++++++++++++++++++                                | 35% ~01m 39s      
   |+++++++++++++++++++                               | 36% ~01m 38s      
   |+++++++++++++++++++                               | 37% ~01m 36s      
   |++++++++++++++++++++                              | 38% ~01m 35s      
   |++++++++++++++++++++                              | 39% ~01m 35s      
   |+++++++++++++++++++++                             | 40% ~01m 33s      
   |+++++++++++++++++++++                             | 41% ~01m 32s      
   |++++++++++++++++++++++                            | 42% ~01m 30s      
   |++++++++++++++++++++++                            | 43% ~01m 28s      
   |+++++++++++++++++++++++                           | 44% ~01m 27s      
   |+++++++++++++++++++++++                           | 45% ~01m 25s      
   |++++++++++++++++++++++++                          | 46% ~01m 24s      
   |++++++++++++++++++++++++                          | 47% ~01m 22s      
   |+++++++++++++++++++++++++                         | 48% ~01m 21s      
   |+++++++++++++++++++++++++                         | 49% ~01m 19s      
   |++++++++++++++++++++++++++                        | 51% ~01m 17s      
   |++++++++++++++++++++++++++                        | 52% ~01m 16s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 14s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 13s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 11s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 10s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 08s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 06s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 05s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 03s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 02s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 01s      
   |++++++++++++++++++++++++++++++++                  | 63% ~59s          
   |++++++++++++++++++++++++++++++++                  | 64% ~58s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~56s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~54s          
   |++++++++++++++++++++++++++++++++++                | 67% ~53s          
   |++++++++++++++++++++++++++++++++++                | 68% ~51s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~49s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~48s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~46s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~45s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~43s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~41s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~40s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~38s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~37s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~35s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~33s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~32s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~30s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~29s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~27s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 36s

   |+                                                 | 1 % ~01m 51s      
   |++                                                | 2 % ~01m 51s      
   |++                                                | 3 % ~01m 50s      
   |+++                                               | 4 % ~01m 52s      
   |+++                                               | 5 % ~01m 50s      
   |++++                                              | 6 % ~01m 49s      
   |++++                                              | 7 % ~01m 47s      
   |+++++                                             | 8 % ~01m 46s      
   |+++++                                             | 9 % ~01m 45s      
   |++++++                                            | 10% ~01m 43s      
   |++++++                                            | 11% ~01m 42s      
   |+++++++                                           | 12% ~01m 41s      
   |+++++++                                           | 13% ~01m 41s      
   |++++++++                                          | 14% ~01m 40s      
   |++++++++                                          | 15% ~01m 38s      
   |+++++++++                                         | 16% ~01m 37s      
   |+++++++++                                         | 18% ~01m 36s      
   |++++++++++                                        | 19% ~01m 34s      
   |++++++++++                                        | 20% ~01m 33s      
   |+++++++++++                                       | 21% ~01m 32s      
   |+++++++++++                                       | 22% ~01m 31s      
   |++++++++++++                                      | 23% ~01m 32s      
   |++++++++++++                                      | 24% ~01m 30s      
   |+++++++++++++                                     | 25% ~01m 29s      
   |+++++++++++++                                     | 26% ~01m 28s      
   |++++++++++++++                                    | 27% ~01m 26s      
   |++++++++++++++                                    | 28% ~01m 25s      
   |+++++++++++++++                                   | 29% ~01m 24s      
   |+++++++++++++++                                   | 30% ~01m 22s      
   |++++++++++++++++                                  | 31% ~01m 21s      
   |++++++++++++++++                                  | 32% ~01m 20s      
   |+++++++++++++++++                                 | 33% ~01m 19s      
   |++++++++++++++++++                                | 34% ~01m 18s      
   |++++++++++++++++++                                | 35% ~01m 16s      
   |+++++++++++++++++++                               | 36% ~01m 15s      
   |+++++++++++++++++++                               | 37% ~01m 14s      
   |++++++++++++++++++++                              | 38% ~01m 13s      
   |++++++++++++++++++++                              | 39% ~01m 11s      
   |+++++++++++++++++++++                             | 40% ~01m 10s      
   |+++++++++++++++++++++                             | 41% ~01m 09s      
   |++++++++++++++++++++++                            | 42% ~01m 08s      
   |++++++++++++++++++++++                            | 43% ~01m 07s      
   |+++++++++++++++++++++++                           | 44% ~01m 06s      
   |+++++++++++++++++++++++                           | 45% ~01m 04s      
   |++++++++++++++++++++++++                          | 46% ~01m 03s      
   |++++++++++++++++++++++++                          | 47% ~01m 02s      
   |+++++++++++++++++++++++++                         | 48% ~01m 01s      
   |+++++++++++++++++++++++++                         | 49% ~59s          
   |++++++++++++++++++++++++++                        | 51% ~59s          
   |++++++++++++++++++++++++++                        | 52% ~58s          
   |+++++++++++++++++++++++++++                       | 53% ~57s          
   |+++++++++++++++++++++++++++                       | 54% ~55s          
   |++++++++++++++++++++++++++++                      | 55% ~54s          
   |++++++++++++++++++++++++++++                      | 56% ~53s          
   |+++++++++++++++++++++++++++++                     | 57% ~52s          
   |+++++++++++++++++++++++++++++                     | 58% ~50s          
   |++++++++++++++++++++++++++++++                    | 59% ~49s          
   |++++++++++++++++++++++++++++++                    | 60% ~48s          
   |+++++++++++++++++++++++++++++++                   | 61% ~47s          
   |+++++++++++++++++++++++++++++++                   | 62% ~45s          
   |++++++++++++++++++++++++++++++++                  | 63% ~44s          
   |++++++++++++++++++++++++++++++++                  | 64% ~43s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~42s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~40s          
   |++++++++++++++++++++++++++++++++++                | 67% ~39s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~38s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~37s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~35s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~34s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~33s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~32s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~31s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~29s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~28s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~27s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~26s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~23s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 58s

   |+                                                 | 1 % ~02m 15s      
   |+                                                 | 2 % ~02m 14s      
   |++                                                | 3 % ~02m 13s      
   |++                                                | 4 % ~02m 14s      
   |+++                                               | 5 % ~02m 12s      
   |+++                                               | 6 % ~02m 10s      
   |++++                                              | 7 % ~02m 08s      
   |++++                                              | 8 % ~02m 07s      
   |+++++                                             | 9 % ~02m 05s      
   |+++++                                             | 10% ~02m 04s      
   |++++++                                            | 11% ~02m 02s      
   |++++++                                            | 12% ~02m 01s      
   |+++++++                                           | 13% ~02m 01s      
   |+++++++                                           | 14% ~02m 01s      
   |++++++++                                          | 15% ~01m 59s      
   |++++++++                                          | 16% ~01m 58s      
   |+++++++++                                         | 17% ~01m 56s      
   |+++++++++                                         | 18% ~01m 55s      
   |++++++++++                                        | 19% ~01m 53s      
   |++++++++++                                        | 20% ~01m 52s      
   |+++++++++++                                       | 21% ~01m 55s      
   |+++++++++++                                       | 22% ~01m 53s      
   |++++++++++++                                      | 23% ~01m 51s      
   |++++++++++++                                      | 24% ~01m 50s      
   |+++++++++++++                                     | 25% ~01m 48s      
   |+++++++++++++                                     | 26% ~01m 46s      
   |++++++++++++++                                    | 27% ~01m 45s      
   |++++++++++++++                                    | 28% ~01m 43s      
   |+++++++++++++++                                   | 29% ~01m 42s      
   |+++++++++++++++                                   | 30% ~01m 40s      
   |++++++++++++++++                                  | 31% ~01m 38s      
   |++++++++++++++++                                  | 32% ~01m 37s      
   |+++++++++++++++++                                 | 33% ~01m 35s      
   |+++++++++++++++++                                | 34% ~01m 34s      
   |++++++++++++++++++                                | 35% ~01m 33s      
   |++++++++++++++++++                                | 36% ~01m 31s      
   |+++++++++++++++++++                               | 37% ~01m 30s      
   |+++++++++++++++++++                               | 38% ~01m 28s      
   |++++++++++++++++++++                              | 39% ~01m 27s      
   |++++++++++++++++++++                              | 40% ~01m 25s      
   |+++++++++++++++++++++                             | 41% ~01m 24s      
   |+++++++++++++++++++++                             | 42% ~01m 22s      
   |++++++++++++++++++++++                            | 43% ~01m 21s      
   |++++++++++++++++++++++                            | 44% ~01m 19s      
   |+++++++++++++++++++++++                           | 45% ~01m 18s      
   |+++++++++++++++++++++++                           | 46% ~01m 16s      
   |++++++++++++++++++++++++                          | 47% ~01m 15s      
   |++++++++++++++++++++++++                          | 48% ~01m 14s      
   |+++++++++++++++++++++++++                         | 49% ~01m 12s      
   |+++++++++++++++++++++++++                         | 50% ~01m 11s      
   |++++++++++++++++++++++++++                        | 51% ~01m 09s      
   |++++++++++++++++++++++++++                        | 52% ~01m 08s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 06s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 05s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 03s      
   |++++++++++++++++++++++++++++                     | 56% ~01m 02s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 01s      
   |+++++++++++++++++++++++++++++                     | 58% ~59s          
   |++++++++++++++++++++++++++++++                    | 59% ~58s          
   |++++++++++++++++++++++++++++++                    | 60% ~56s          
   |+++++++++++++++++++++++++++++++                   | 61% ~55s          
   |+++++++++++++++++++++++++++++++                   | 62% ~53s          
   |++++++++++++++++++++++++++++++++                  | 63% ~52s          
   |++++++++++++++++++++++++++++++++                  | 64% ~51s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~49s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~48s          
   |++++++++++++++++++++++++++++++++++                | 67% ~46s          
   |++++++++++++++++++++++++++++++++++               | 68% ~45s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~44s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~42s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~41s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~39s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~38s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~36s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~35s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~34s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~32s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~31s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~30s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~28s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~27s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~24s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~22s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~21s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 22s

   |+                                                 | 1 % ~01m 17s      
   |++                                                | 2 % ~01m 16s      
   |++                                                | 3 % ~01m 16s      
   |+++                                               | 4 % ~01m 15s      
   |+++                                               | 5 % ~01m 14s      
   |++++                                              | 6 % ~01m 13s      
   |++++                                              | 7 % ~01m 12s      
   |+++++                                             | 8 % ~01m 12s      
   |+++++                                             | 9 % ~01m 11s      
   |++++++                                            | 10% ~01m 10s      
   |++++++                                            | 11% ~01m 09s      
   |+++++++                                           | 12% ~01m 09s      
   |+++++++                                           | 13% ~01m 09s      
   |++++++++                                          | 14% ~01m 08s      
   |++++++++                                          | 15% ~01m 07s      
   |+++++++++                                         | 16% ~01m 06s      
   |+++++++++                                         | 18% ~01m 05s      
   |++++++++++                                        | 19% ~01m 04s      
   |++++++++++                                        | 20% ~01m 04s      
   |+++++++++++                                       | 21% ~01m 03s      
   |+++++++++++                                       | 22% ~01m 02s      
   |++++++++++++                                      | 23% ~01m 01s      
   |++++++++++++                                      | 24% ~01m 00s      
   |+++++++++++++                                     | 25% ~60s          
   |+++++++++++++                                     | 26% ~59s          
   |++++++++++++++                                    | 27% ~58s          
   |++++++++++++++                                    | 28% ~57s          
   |+++++++++++++++                                   | 29% ~56s          
   |+++++++++++++++                                   | 30% ~55s          
   |++++++++++++++++                                  | 31% ~55s          
   |++++++++++++++++                                  | 32% ~54s          
   |+++++++++++++++++                                 | 33% ~53s          
   |++++++++++++++++++                                | 34% ~52s          
   |++++++++++++++++++                                | 35% ~52s          
   |+++++++++++++++++++                               | 36% ~51s          
   |+++++++++++++++++++                               | 37% ~50s          
   |++++++++++++++++++++                              | 38% ~49s          
   |++++++++++++++++++++                              | 39% ~48s          
   |+++++++++++++++++++++                             | 40% ~47s          
   |+++++++++++++++++++++                             | 41% ~47s          
   |++++++++++++++++++++++                            | 42% ~46s          
   |++++++++++++++++++++++                            | 43% ~45s          
   |+++++++++++++++++++++++                           | 44% ~44s          
   |+++++++++++++++++++++++                           | 45% ~43s          
   |++++++++++++++++++++++++                          | 46% ~42s          
   |++++++++++++++++++++++++                          | 47% ~42s          
   |+++++++++++++++++++++++++                         | 48% ~41s          
   |+++++++++++++++++++++++++                         | 49% ~40s          
   |++++++++++++++++++++++++++                        | 51% ~39s          
   |++++++++++++++++++++++++++                        | 52% ~38s          
   |+++++++++++++++++++++++++++                       | 53% ~38s          
   |+++++++++++++++++++++++++++                       | 54% ~37s          
   |++++++++++++++++++++++++++++                      | 55% ~36s          
   |++++++++++++++++++++++++++++                      | 56% ~35s          
   |+++++++++++++++++++++++++++++                     | 57% ~34s          
   |+++++++++++++++++++++++++++++                     | 58% ~33s          
   |++++++++++++++++++++++++++++++                    | 59% ~33s          
   |++++++++++++++++++++++++++++++                    | 60% ~32s          
   |+++++++++++++++++++++++++++++++                   | 61% ~31s          
   |+++++++++++++++++++++++++++++++                   | 62% ~30s          
   |++++++++++++++++++++++++++++++++                  | 63% ~29s          
   |++++++++++++++++++++++++++++++++                  | 64% ~29s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~28s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~27s          
   |++++++++++++++++++++++++++++++++++                | 67% ~26s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~25s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~25s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~24s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~23s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~22s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~21s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~20s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~20s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~19s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~18s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~17s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 20s

   |+                                                 | 1 % ~50s          
   |++                                                | 2 % ~49s          
   |++                                                | 3 % ~49s          
   |+++                                               | 4 % ~49s          
   |+++                                               | 5 % ~48s          
   |++++                                              | 7 % ~48s          
   |++++                                              | 8 % ~47s          
   |+++++                                             | 9 % ~46s          
   |+++++                                             | 10% ~47s          
   |++++++                                            | 11% ~46s          
   |++++++                                            | 12% ~45s          
   |+++++++                                           | 13% ~45s          
   |++++++++                                          | 14% ~44s          
   |++++++++                                          | 15% ~44s          
   |+++++++++                                         | 16% ~43s          
   |+++++++++                                         | 17% ~42s          
   |++++++++++                                        | 18% ~42s          
   |++++++++++                                        | 20% ~41s          
   |+++++++++++                                       | 21% ~41s          
   |+++++++++++                                       | 22% ~40s          
   |++++++++++++                                      | 23% ~39s          
   |++++++++++++                                      | 24% ~39s          
   |+++++++++++++                                     | 25% ~38s          
   |++++++++++++++                                    | 26% ~38s          
   |++++++++++++++                                    | 27% ~37s          
   |+++++++++++++++                                   | 28% ~37s          
   |+++++++++++++++                                   | 29% ~36s          
   |++++++++++++++++                                  | 30% ~36s          
   |++++++++++++++++                                  | 32% ~35s          
   |+++++++++++++++++                                 | 33% ~34s          
   |+++++++++++++++++                                 | 34% ~34s          
   |++++++++++++++++++                                | 35% ~33s          
   |++++++++++++++++++                                | 36% ~33s          
   |+++++++++++++++++++                               | 37% ~32s          
   |++++++++++++++++++++                              | 38% ~32s          
   |++++++++++++++++++++                              | 39% ~31s          
   |+++++++++++++++++++++                             | 40% ~31s          
   |+++++++++++++++++++++                             | 41% ~30s          
   |++++++++++++++++++++++                            | 42% ~30s          
   |++++++++++++++++++++++                            | 43% ~29s          
   |+++++++++++++++++++++++                           | 45% ~29s          
   |+++++++++++++++++++++++                           | 46% ~28s          
   |++++++++++++++++++++++++                          | 47% ~27s          
   |++++++++++++++++++++++++                          | 48% ~27s          
   |+++++++++++++++++++++++++                         | 49% ~26s          
   |+++++++++++++++++++++++++                         | 50% ~26s          
   |++++++++++++++++++++++++++                        | 51% ~25s          
   |+++++++++++++++++++++++++++                       | 52% ~25s          
   |+++++++++++++++++++++++++++                       | 53% ~24s          
   |++++++++++++++++++++++++++++                      | 54% ~24s          
   |++++++++++++++++++++++++++++                      | 55% ~23s          
   |+++++++++++++++++++++++++++++                     | 57% ~22s          
   |+++++++++++++++++++++++++++++                     | 58% ~22s          
   |++++++++++++++++++++++++++++++                    | 59% ~21s          
   |++++++++++++++++++++++++++++++                    | 60% ~21s          
   |+++++++++++++++++++++++++++++++                   | 61% ~20s          
   |+++++++++++++++++++++++++++++++                   | 62% ~20s          
   |++++++++++++++++++++++++++++++++                  | 63% ~19s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~18s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~18s          
   |++++++++++++++++++++++++++++++++++                | 66% ~17s          
   |++++++++++++++++++++++++++++++++++                | 67% ~17s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~16s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~16s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~15s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~15s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~14s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~13s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~13s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~12s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~12s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~11s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++         | 80% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~10s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~06s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 52s

t-SNE plots with cluster identity or with age

Color the tSNE plot by the age of the animals

x <- FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("slateblue","yellowgreen") , no.legend = FALSE , do.return = TRUE )

tsne_ageplot <- ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal()
plotsize = 5
tsne_ageplot

#ggsave(plot = tsne_ageplot , filename = "age_tsne_hires.png" , width = plotsize , height = plotsize )
ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal() # + guides(color = "none")

Color the tSNE by the identified celltypes and subpopulations

g <- TSNEPlot(seurat_10X2 , do.return = TRUE)
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) + 
  geom_point(size = 1) + 
  labs( x = "tSNE_1" , y = "tSNE_2" ) + 
  coord_equal() + 
  scale_color_manual(values =   c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )

ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) + 
  geom_point(size = 1) + 
  labs( x = "tSNE_1" , y = "tSNE_2" ) + 
  coord_equal() + 
  # guides(color = "none") + 
  scale_color_manual(values =   c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )

Save the analysis results so far as RDS file

# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene_all_cells.RDS")

Identify and filter out contaminating ependymal cells and leukocytes

We find that there are two small clusters in the tSNE plot that lie outside the qNSC2 and the aNSC0. Using the interactive visualisation feature we can determine the names of these cells and check for differentially expressed genes from those.

outside_q2 <- c("GCGAGAATCGCTTAGA-1", "GTTAAGCGTGCACTTA-1", "TCATTTGTCGTCCAGG-1", 
"AACGTTGCACGCCAGT-2", "CAACTAGAGTCGCCGT-2", "CCACTACTCGGAATCT-2", 
"CCGTTCATCCTGCTTG-2", "CGCTGGAGTAGTACCT-2", "TACTTACTCCTAGGGC-2", 
"TAGTTGGCACATTAGC-2", "TGGCTGGCACTCAGGC-2", "TGTATTCCATGTTGAC-2"
)
outside_a0 <- c("ATGGGAGAGATCCGAG-1", "TACACGACAAAGTGCG-1", "CCATGTCTCCTAGGGC-2", 
"CGTGAGCCACCATGTA-2", "CTCGAAAAGTGCGATG-2", "TGACGGCCATCGATGT-2"
)
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_a0 , ident.use = "outside_a0")
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_q2 , ident.use = "outside_q2")
TSNEPlot(object = seurat_10X2 , do.label = TRUE)

First the cells outside aNSC0

outside_a0_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_a0")

   |+                                                 | 1 % ~05m 26s      
   |+                                                 | 2 % ~05m 19s      
   |++                                                | 3 % ~05m 15s      
   |++                                                | 4 % ~05m 13s      
   |+++                                               | 5 % ~05m 08s      
   |+++                                               | 6 % ~05m 03s      
   |++++                                              | 7 % ~04m 59s      
   |++++                                              | 8 % ~04m 56s      
   |+++++                                             | 9 % ~04m 53s      
   |+++++                                             | 10% ~04m 49s      
   |++++++                                            | 11% ~04m 47s      
   |++++++                                            | 12% ~04m 45s      
   |+++++++                                           | 13% ~04m 41s      
   |+++++++                                           | 14% ~04m 38s      
   |++++++++                                          | 15% ~04m 35s      
   |++++++++                                          | 16% ~04m 31s      
   |+++++++++                                         | 17% ~04m 28s      
   |+++++++++                                         | 18% ~04m 25s      
   |++++++++++                                        | 19% ~04m 22s      
   |++++++++++                                        | 20% ~04m 19s      
   |+++++++++++                                       | 21% ~04m 16s      
   |+++++++++++                                       | 22% ~04m 13s      
   |++++++++++++                                      | 23% ~04m 09s      
   |++++++++++++                                      | 24% ~04m 06s      
   |+++++++++++++                                     | 25% ~04m 03s      
   |+++++++++++++                                     | 26% ~03m 60s      
   |++++++++++++++                                    | 27% ~03m 56s      
   |++++++++++++++                                    | 28% ~03m 53s      
   |+++++++++++++++                                   | 29% ~03m 50s      
   |+++++++++++++++                                   | 30% ~03m 47s      
   |++++++++++++++++                                  | 31% ~03m 44s      
   |++++++++++++++++                                  | 32% ~03m 40s      
   |+++++++++++++++++                                 | 33% ~03m 37s      
   |+++++++++++++++++                                | 34% ~03m 34s      
   |++++++++++++++++++                                | 35% ~03m 31s      
   |++++++++++++++++++                                | 36% ~03m 27s      
   |+++++++++++++++++++                               | 37% ~03m 24s      
   |+++++++++++++++++++                               | 38% ~03m 21s      
   |++++++++++++++++++++                              | 39% ~03m 17s      
   |++++++++++++++++++++                              | 40% ~03m 14s      
   |+++++++++++++++++++++                             | 41% ~03m 11s      
   |+++++++++++++++++++++                             | 42% ~03m 08s      
   |++++++++++++++++++++++                            | 43% ~03m 05s      
   |++++++++++++++++++++++                            | 44% ~03m 01s      
   |+++++++++++++++++++++++                           | 45% ~02m 58s      
   |+++++++++++++++++++++++                           | 46% ~02m 55s      
   |++++++++++++++++++++++++                          | 47% ~02m 52s      
   |++++++++++++++++++++++++                          | 48% ~02m 49s      
   |+++++++++++++++++++++++++                         | 49% ~02m 46s      
   |+++++++++++++++++++++++++                         | 50% ~02m 43s      
   |++++++++++++++++++++++++++                        | 51% ~02m 39s      
   |++++++++++++++++++++++++++                        | 52% ~02m 36s      
   |+++++++++++++++++++++++++++                       | 53% ~02m 33s      
   |+++++++++++++++++++++++++++                       | 54% ~02m 30s      
   |++++++++++++++++++++++++++++                      | 55% ~02m 27s      
   |++++++++++++++++++++++++++++                     | 56% ~02m 23s      
   |+++++++++++++++++++++++++++++                     | 57% ~02m 20s      
   |+++++++++++++++++++++++++++++                     | 58% ~02m 17s      
   |++++++++++++++++++++++++++++++                    | 59% ~02m 13s      
   |++++++++++++++++++++++++++++++                    | 60% ~02m 10s      
   |+++++++++++++++++++++++++++++++                   | 61% ~02m 07s      
   |+++++++++++++++++++++++++++++++                   | 62% ~02m 04s      
   |++++++++++++++++++++++++++++++++                  | 63% ~02m 00s      
   |++++++++++++++++++++++++++++++++                  | 64% ~01m 57s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 54s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~01m 51s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 47s      
   |++++++++++++++++++++++++++++++++++               | 68% ~01m 44s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 41s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~01m 38s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 34s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 31s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01m 28s      
   |+++++++++++++++++++++++++++++++++++++             | 74% ~01m 25s      
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01m 21s      
   |++++++++++++++++++++++++++++++++++++++            | 76% ~01m 18s      
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~01m 15s      
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~01m 12s      
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~01m 08s      
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~01m 05s      
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01m 02s      
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~59s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~55s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~52s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~49s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~46s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~42s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~39s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~36s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~33s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~29s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~26s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~23s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05m 25s
outside_a0_markers

Checking the differentially expressed genes we can see many leukocyte marker genes, amongst them CD45.

## Ptprc = CD45 -> Leukocyte Common Antigen (LCA)
VlnPlot(object = seurat_10X2 ,  features.plot = c("Ptprc","Slc1a3") , x.lab.rot = TRUE )

Next the cells outside qNSC2

outside_q2_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_q2")

   |+                                                 | 1 % ~03m 40s      
   |+                                                 | 2 % ~03m 41s      
   |++                                                | 3 % ~03m 37s      
   |++                                                | 4 % ~03m 34s      
   |+++                                               | 5 % ~03m 31s      
   |+++                                               | 6 % ~03m 29s      
   |++++                                              | 7 % ~03m 26s      
   |++++                                              | 8 % ~03m 24s      
   |+++++                                             | 9 % ~03m 22s      
   |+++++                                             | 10% ~03m 19s      
   |++++++                                            | 11% ~03m 18s      
   |++++++                                            | 12% ~03m 15s      
   |+++++++                                           | 13% ~03m 13s      
   |+++++++                                           | 14% ~03m 11s      
   |++++++++                                          | 15% ~03m 09s      
   |++++++++                                          | 16% ~03m 07s      
   |+++++++++                                         | 17% ~03m 04s      
   |+++++++++                                         | 18% ~03m 02s      
   |++++++++++                                        | 19% ~03m 01s      
   |++++++++++                                        | 20% ~02m 59s      
   |+++++++++++                                       | 21% ~02m 56s      
   |+++++++++++                                       | 22% ~02m 54s      
   |++++++++++++                                      | 23% ~02m 52s      
   |++++++++++++                                      | 24% ~02m 49s      
   |+++++++++++++                                     | 25% ~02m 47s      
   |+++++++++++++                                     | 26% ~02m 45s      
   |++++++++++++++                                    | 27% ~02m 43s      
   |++++++++++++++                                    | 28% ~02m 41s      
   |+++++++++++++++                                   | 29% ~02m 38s      
   |+++++++++++++++                                   | 30% ~02m 36s      
   |++++++++++++++++                                  | 31% ~02m 34s      
   |++++++++++++++++                                  | 32% ~02m 31s      
   |+++++++++++++++++                                 | 33% ~02m 29s      
   |+++++++++++++++++                                | 34% ~02m 27s      
   |++++++++++++++++++                                | 35% ~02m 25s      
   |++++++++++++++++++                                | 36% ~02m 23s      
   |+++++++++++++++++++                               | 37% ~02m 20s      
   |+++++++++++++++++++                               | 38% ~02m 18s      
   |++++++++++++++++++++                              | 39% ~02m 16s      
   |++++++++++++++++++++                              | 40% ~02m 14s      
   |+++++++++++++++++++++                             | 41% ~02m 12s      
   |+++++++++++++++++++++                             | 42% ~02m 09s      
   |++++++++++++++++++++++                            | 43% ~02m 07s      
   |++++++++++++++++++++++                            | 44% ~02m 05s      
   |+++++++++++++++++++++++                           | 45% ~02m 03s      
   |+++++++++++++++++++++++                           | 46% ~02m 01s      
   |++++++++++++++++++++++++                          | 47% ~01m 58s      
   |++++++++++++++++++++++++                          | 48% ~01m 56s      
   |+++++++++++++++++++++++++                         | 49% ~01m 54s      
   |+++++++++++++++++++++++++                         | 50% ~01m 52s      
   |++++++++++++++++++++++++++                        | 51% ~01m 50s      
   |++++++++++++++++++++++++++                        | 52% ~01m 48s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 45s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 43s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 41s      
   |++++++++++++++++++++++++++++                     | 56% ~01m 39s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 37s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 34s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 32s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 30s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 28s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 25s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 23s      
   |++++++++++++++++++++++++++++++++                  | 64% ~01m 21s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 19s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~01m 16s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 15s      
   |++++++++++++++++++++++++++++++++++               | 68% ~01m 12s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 10s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~01m 08s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 05s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 03s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01m 01s      
   |+++++++++++++++++++++++++++++++++++++             | 74% ~59s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~56s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~54s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~52s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~50s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~47s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~45s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~43s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~40s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~38s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~36s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~34s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~31s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~29s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~27s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 42s
outside_q2_markers

Checking the differentially expressed genes we can see ependymal cell genes (Genes from https://pdfs.semanticscholar.org/e833/5f8154f714088147d76701ee0052d3a388b5.pdf)

VlnPlot(object = seurat_10X2 ,  features.plot = c("Iqca","Bbox1","Apoe","Syne1","Meig1","Foxj1","Slc1a3") , x.lab.rot = TRUE )

Next we exclude these contaminating cells and save the new seurat objects as a RDS file.

cells_to_remove <- WhichCells(object = seurat_10X2 , ident = c("outside_q2","outside_a0"))
cells_to_remove
 [1] "GCGAGAATCGCTTAGA-1" "GTTAAGCGTGCACTTA-1" "TCATTTGTCGTCCAGG-1"
 [4] "AACGTTGCACGCCAGT-2" "CAACTAGAGTCGCCGT-2" "CCACTACTCGGAATCT-2"
 [7] "CCGTTCATCCTGCTTG-2" "CGCTGGAGTAGTACCT-2" "TACTTACTCCTAGGGC-2"
[10] "TAGTTGGCACATTAGC-2" "TGGCTGGCACTCAGGC-2" "TGTATTCCATGTTGAC-2"
[13] "ATGGGAGAGATCCGAG-1" "TACACGACAAAGTGCG-1" "CCATGTCTCCTAGGGC-2"
[16] "CGTGAGCCACCATGTA-2" "CTCGAAAAGTGCGATG-2" "TGACGGCCATCGATGT-2"

After we made sure we know which kind of celltype these cells are, we can get rid of them in the following analysis

all_cells <- WhichCells(object = seurat_10X2 )
cells_to_keep <- all_cells[! all_cells %in% cells_to_remove]
seurat_10X2 <- SubsetData(object = seurat_10X2 , cells.use = cells_to_keep )
TSNEPlot(object = seurat_10X2 , do.label = TRUE)

Save the results as an RDS file

# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene.RDS")

SessionInfo

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods  
[9] base     

other attached packages:
 [1] org.Mm.eg.db_3.5.0    AnnotationDbi_1.40.0  IRanges_2.12.0       
 [4] S4Vectors_0.16.0      bindrcpp_0.2          ReactomePA_1.22.0    
 [7] clusterProfiler_3.4.4 DOSE_3.4.0            RColorBrewer_1.1-2   
[10] pheatmap_1.0.10       kableExtra_0.9.0      knitr_1.20           
[13] Seurat_2.2.0          Matrix_1.2-14         cowplot_0.7.0        
[16] forcats_0.3.0         stringr_1.3.1         dplyr_0.7.4          
[19] purrr_0.2.4           readr_1.1.1           tidyr_0.7.2          
[22] tibble_1.4.2          ggplot2_2.2.1         tidyverse_1.2.1      
[25] Biobase_2.38.0        BiocGenerics_0.24.0  

loaded via a namespace (and not attached):
  [1] utf8_1.1.4           R.utils_2.6.0        lme4_1.1-18-1       
  [4] RSQLite_2.1.1        htmlwidgets_1.2      grid_3.4.3          
  [7] trimcluster_0.1-2    ranger_0.6.0         BiocParallel_1.12.0 
 [10] Rtsne_0.11           munsell_0.4.3        codetools_0.2-15    
 [13] ica_1.0-2            colorspace_1.3-2     GOSemSim_2.4.1      
 [16] rstudioapi_0.7       ROCR_1.0-7           robustbase_0.92-7   
 [19] dtw_1.20-1           NMF_0.20.6           labeling_0.3        
 [22] lars_1.2             mnormt_1.5-5         bit64_0.9-7         
 [25] rprojroot_1.3-2      diptest_0.75-7       R6_2.2.2            
 [28] doParallel_1.0.10    VGAM_1.0-3           flexmix_2.3-13      
 [31] bitops_1.0-6         fgsea_1.4.1          assertthat_0.2.0    
 [34] SDMTools_1.1-221     scales_0.5.0         nnet_7.3-12         
 [37] gtable_0.2.0         rlang_0.2.2          MatrixModels_0.4-1  
 [40] scatterplot3d_0.3-41 splines_3.4.3        lazyeval_0.2.0      
 [43] ModelMetrics_1.1.0   acepack_1.4.1        broom_0.4.3         
 [46] checkmate_1.8.5      yaml_2.2.0           reshape2_1.4.2      
 [49] abind_1.4-5          modelr_0.1.1         backports_1.1.2     
 [52] qvalue_2.10.0        Hmisc_4.1-1          caret_6.0-73        
 [55] tools_3.4.3          psych_1.7.8          gridBase_0.4-7      
 [58] gplots_3.0.1         proxy_0.4-22         ggridges_0.5.0      
 [61] Rcpp_0.12.18         plyr_1.8.4           base64enc_0.1-3     
 [64] rpart_4.1-12         pbapply_1.3-1        haven_1.1.2         
 [67] cluster_2.0.6        magrittr_1.5         data.table_1.11.6   
 [70] DO.db_2.9            openxlsx_4.1.0       reactome.db_1.62.0  
 [73] mvtnorm_1.0-5        hms_0.4.2            evaluate_0.11       
 [76] xtable_1.8-2         rio_0.5.10           mclust_5.2.2        
 [79] readxl_1.1.0         gridExtra_2.2.1      compiler_3.4.3      
 [82] KernSmooth_2.23-15   crayon_1.3.4         minqa_1.2.4         
 [85] R.oo_1.22.0          htmltools_0.3.6      segmented_0.5-1.4   
 [88] Formula_1.2-3        tclust_1.2-3         lubridate_1.7.1     
 [91] DBI_1.0.0            diffusionMap_1.1-0.1 MASS_7.3-48         
 [94] fpc_2.1-10           rappdirs_0.3.1       boot_1.3-20         
 [97] car_3.0-2            cli_1.0.0            R.methodsS3_1.7.1   
[100] gdata_2.17.0         bindr_0.1            igraph_1.0.1        
[103] pkgconfig_2.0.2      sn_1.5-0             rvcheck_0.1.0       
[106] registry_0.3         numDeriv_2016.8-1    foreign_0.8-69      
[109] xml2_1.1.1           foreach_1.4.3        rngtools_1.2.4      
[112] pkgmaker_0.22        rvest_0.3.2          digest_0.6.12       
[115] tsne_0.1-3           graph_1.56.0         rmarkdown_1.10      
[118] cellranger_1.1.0     fastmatch_1.1-0      htmlTable_1.12      
[121] curl_3.2             kernlab_0.9-25       gtools_3.5.0        
[124] modeltools_0.2-22    graphite_1.24.1      nloptr_1.0.4        
[127] nlme_3.1-131         jsonlite_1.5         carData_3.0-1       
[130] fansi_0.3.0          viridisLite_0.3.0    pillar_1.3.0        
[133] lattice_0.20-35      httr_1.3.1           DEoptimR_1.0-8      
[136] survival_2.41-3      GO.db_3.5.0          glue_1.3.0          
[139] zip_1.0.0            FNN_1.1              prabclus_2.2-6      
[142] iterators_1.0.8      bit_1.1-14           class_7.3-14        
[145] stringi_1.2.4        mixtools_1.0.4       blob_1.1.1          
[148] latticeExtra_0.6-28  caTools_1.17.1       memoise_1.0.0       
[151] irlba_2.1.2          ape_5.1             
---
title: "Analysis of Neural Stem Cells from the subventricular zone sequenced by 10X Genomics 3' Chromium protocol"
output: 
  html_notebook: 
    smart: no
    toc: yes
    toc_float: yes
    df_print: paged
---

# Analysis

## Preparations

### Loading the neccessary packages

```{r}
library("tidyverse")
library("Seurat")
library("Matrix")

library("stringr")
library("knitr")
library("kableExtra")

library("pheatmap")
library("RColorBrewer")

library("clusterProfiler")
library("ReactomePA")
```


```{r}
save_csv <- TRUE
```


<style>
  .border-right {
    border-right: 1px solid grey;
  }
</style>


### Setup vectors needed for the analysis: Genes which are markers for the cell types

```{r}
markers.biol.validated <- c("Thbs4","Cxcl14","Cd9","Nr2e1","Id2","Ascl1","Egfr","Dcx","Dlx1","Mki67","Sox2","S100b","Cd24a","Ift88","Foxj1","Pdgfrb","Pecam1","Slc1a3","Gfap","Nes","Cxcl10","Rpl32","Hes1","Hes5")

markers.custom.order <- c("Sfrp5","Bmpr1a","Vcam1","Slc1a3","Id2","Hes1","Hes5","Egfr","Ascl1","Mki67","Rpl22","Cd9","Nr2e1","Sox2","S100b")
```

### Load a list of cell cycle specific genes (taken from: http://satijalab.org/seurat/cell_cycle_vignette.html)

```{r}
cc.genes <- readLines(con = "cell_cycle_genes/cell_cycle_vignette_files/regev_lab_cell_cycle_genes.txt")
cc.genes <- str_to_title(cc.genes)

# Separate markers of G2/M phase and markers of S phase
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]
```

### Loading the data

```{r}
datadir <- "count_table/filtered_gene_bc_matrices_mex/mm10/"

data_10X2 <- Read10X(data.dir = file.path( datadir) )
```

### Create a Seurat object with the 10X data

```{r}
seurat_10X2 <- CreateSeuratObject(raw.data = data_10X2, min.cells = 3, min.genes = 1500, project = "young_vs_old_10X2")
```

### Add Metadata and check QC values

```{r}
mito.genes <- grep(pattern = "^mt-", x = rownames(x = seurat_10X2@data), value = TRUE)

percent.mito <- Matrix::colSums(seurat_10X2@raw.data[mito.genes, ])/Matrix::colSums(seurat_10X2@raw.data)

seurat_10X2 <- AddMetaData(object = seurat_10X2, metadata = percent.mito, col.name = "percent.mito")

VlnPlot(object = seurat_10X2, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3 )
```

### Annotate the cells with the age of the animals

We can also annotate the age of the mouse from which the cells were prepared (young or old). We know that all cell barcodes ending in ...-1 are beloning to cells from old old mice, the cell barcodes ending in ...-2 are from young mice.

```{r annotate_age ,cache=TRUE}
age.cells <- data.frame( age = as.factor(rownames(seurat_10X2@meta.data)) ) 
rownames(age.cells) <- rownames(seurat_10X2@meta.data) 

age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-1$" , replacement = "old")
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-2$" , replacement = "young")

age.cells$age_num <- age.cells$age
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "old" , replacement = "1")
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "young" , replacement = "2")
age.cells$age_num <- as.numeric(age.cells$age_num)

age.cells$age <- factor(age.cells$age)

seurat_10X2 <- AddMetaData(seurat_10X2, age.cells , "age")
```

### Check scatterplots of nUMI, nGene and % of mitochondrial genes

```{r}
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
```

### Filter the cells which have too high mitochondrial content

```{r}
seurat_10X2 <- FilterCells(object = seurat_10X2, subset.names = c("nGene", "percent.mito"), 
    low.thresholds = c(1500, -Inf), high.thresholds = c(4500, 0.10))
```

### Check scatterplots of nUMI, nGene and % of mitochondrial genes after filtering

```{r}
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
```

### Log Normalize the data

```{r}
seurat_10X2 <- NormalizeData(object = seurat_10X2, normalization.method = "LogNormalize", scale.factor = 10000)
```

### Scale the data and regress out the influence of nUMI and percent.mito

```{r results='hide'}
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = c("nUMI", "percent.mito" , "nGene"))
```

### Regress out difference between S phase and G2/M Phase

```{r}
seurat_10X2 <- CellCycleScoring(object = seurat_10X2, s.genes = s.genes, g2m.genes = g2m.genes, set.ident = TRUE)
```

#### Meta data columns

We can see that the columns S.Score, G2M.Score and Phase are now added to the meta data table

```{r}
head(seurat_10X2@meta.data)
```

```{r}
seurat_10X2@meta.data$CC.Difference <- seurat_10X2@meta.data$S.Score - seurat_10X2@meta.data$G2M.Score
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = "CC.Difference", display.progress = FALSE)
```


## Dimensional reduction and clustering

### Find variable genes

```{r}
seurat_10X2 <- FindVariableGenes(object = seurat_10X2, mean.function = ExpMean, dispersion.function = LogVMR, 
    x.low.cutoff = 0.0125, x.high.cutoff = 4, y.cutoff = 0.5)
```

Using these parameters we have identified 2236 genes as variable.


### Run PCA

Now we use the detected variable genes to peform PCA on the data

```{r results='hide'}
seurat_10X2 <- RunPCA(object = seurat_10X2, pc.genes = seurat_10X2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5  , seed.use = 2 )
seurat_10X2 <- ProjectPCA(object = seurat_10X2 )
seurat_10X2 <- JackStraw(object = seurat_10X2  )
```

### Elbowplot PCA

```{r}
PCElbowPlot(object = seurat_10X2)
```

### PCA Plot

```{r}
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)

```

### Run t-SNE
```{r results='hide', echo=FALSE  }
seurat_10X2 <- RunTSNE(seurat_10X2, dims.use = 1:8, do.fast = T , seed.use = 1)

TSNEPlot(object = seurat_10X2)
```

## Use graph-based clustering to cluster the cells in our dataset

```{r results='hide'}
seurat_10X2 <- FindClusters(seurat_10X2, reduction.type = "pca", dims.use = 1:8, save.SNN = T , force.recalc = TRUE)
```

### Plot t-SNE plot

```{r}
TSNEPlot(object = seurat_10X2)

FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("red","forestgreen") , no.legend = FALSE ) 

FeaturePlot(object = seurat_10X2 , features.plot = "nUMI" , cols.use = c("lightgrey","red") , no.legend = FALSE )

FeaturePlot(object = seurat_10X2 , features.plot = "nGene" , cols.use = c("lightgrey","red") , no.legend = FALSE )

```

Let's look at the 

```{r}
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2, print.bar = FALSE , only.pos = TRUE , return.thresh = 0.05)

markers.seurat_10X2 %>% filter(cluster == 0)

markers.seurat_10X2 %>% filter(cluster == 1)

markers.seurat_10X2 %>% filter(cluster == 2)

markers.seurat_10X2 %>% filter(cluster == 3)

markers.seurat_10X2 %>% filter(cluster == 4)

markers.seurat_10X2 %>% filter(cluster == 5)

markers.seurat_10X2 %>% filter(cluster == 6)

markers.seurat_10X2 %>% filter(cluster == 7)

markers.seurat_10X2 %>% filter(cluster == 8)

markers.seurat_10X2 %>% filter(cluster == 9)

markers.seurat_10X2 %>% filter(cluster == 10)
```

### Differential gene expression between cluster 6 and 8

```{r}
markers_cluster_6_vs_8 <- FindMarkers(object = seurat_10X2 , ident.1 = 6 , ident.2 = 8 , only.pos = TRUE , print.bar = FALSE )

markers_cluster_6_vs_8
```

Cluster 6 shows increased expression of Mapk-Pathway related genes, immediate-early genes and transcription factors, like: Jun, Fos, Atf3, Ier2 ... 

```{r}
markers_cluster_8_vs_6 <- FindMarkers(object = seurat_10X2 , ident.1 = 8  , ident.2 = 6 , only.pos = TRUE , print.bar = FALSE )

markers_cluster_8_vs_6
```


### tSNE feature plots: marker genes

```{r}
TSNEPlot(object = seurat_10X2)
## quiescent Markers
FeaturePlot(object = seurat_10X2 , features.plot = "Thbs4" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Id2" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl14" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Stem Cell markers
FeaturePlot(object = seurat_10X2 , features.plot = "Slc1a3" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nr2e1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nes" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Proliferation markers
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl22" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl32" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cd9" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Astrocyte markers
FeaturePlot(object = seurat_10X2 , features.plot = "S100b" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Gfap" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Active NSC markers
FeaturePlot(object = seurat_10X2 , features.plot = "Mki67" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Ascl1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Egfr" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## TAPs
FeaturePlot(object = seurat_10X2 , features.plot = "Vcam1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Dlx1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Neuroblast marker
FeaturePlot(object = seurat_10X2 , features.plot = "Dcx" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Sfrp5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Oligodenrocyte marker
FeaturePlot(object = seurat_10X2 , features.plot = "Olig1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Pdgfra" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Sox10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
```

### Check tSNE Plot with markers found by Basak et al. (2018)

```{r}  
# Load the genes as vector
markers <- c( "Agt","Slc6a9","Etnppl","Slc6a1","Sparc",
                            "Slc1a3","Bcan","Tspan7","Htra1","Cldn10","Ptn","Acsl6","Fgfr3",
                            "Sparcl1","Atp1a2","Gpr37l1","Gja1","Prnp","Acsl3","Aqp4","Apoe","Gm26917",       # markers for quiescent NSCs - Gm26917 = Rn45s
                            "Cst3","Clu","Slc1a2","Prdx6","Mt1","Aldoc",                                    # shared between quiescent and primed NSCs - slc1a2 instead of scl1a2 (probably typo)
                            "Thbs4","Ntrk2","Fxyd1","Gstm1","Igfbp5","S100a6","Itm2b","Sfrp1","Dkk3","C4b",
                            "Acot1","Luc7l3","Ckb",
                            "Cpe","Dbi",                                                                    # primed NSCs and early active NSCs
                            "Miat","Lima1","Pabpc1","Ascl1","Rpl12","Mycn","Olig2",
                            "Pcna","Hsp90aa1","Hnrnpab","Ran","Ppia",
                            "Eef1a1","Ptma","Rpl41","Npm1", "Rpsa", "Fabp7", "Egfr",                        # active NSCs
                            "Mki67","Dlx2","Dlx1","Cdca3","Dlx1as",
                            "Nrep","Tubb2b","Dcx","Btg1","Nfib",
                            "Gad1","Ndrg4","Snap25","Syt1","Rbfox3",
                            "Tmsb10","Stmn2","Cd24a","Dlx6os1","Tubb5","Tubb3","Ccnd2","Hmgn2","H2afz","Sox11","Tuba1b","Tmsb4x","Stmn1","Tpt1","Rpl18a"
                            )  

```


```{r}  
interval <- c( seq( 1, length(markers) , by = round(length(markers)/4) ) , length(markers) )

interval
```

#### a) Ascl6 - Clu

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[1] , interval[2] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

#### b) Sfrp1 - Ascl1

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[2]+1 , interval[3] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

#### c) Npm1 - Dcx

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[3]+1 , interval[4] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

#### d) Tubb5 - Rpl18a

```{r}  
TSNEPlot(object = seurat_10X2)

for(i in seq( interval[4]+1 , interval[5] )){
  FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
```

### Violin plots: marker genes

```{r}
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" )
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl14" )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" )
## Active NSC markers & TAPS
VlnPlot(object = seurat_10X2 , features.plot = "Mki67" )
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" )
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx2" )
VlnPlot(object = seurat_10X2 , features.plot = "Atp1a2" )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" )
```

### Heatmap marker genes

```{r fig.width=16 , fig.height=8}
markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )

DoHeatmap(object = seurat_10X2 , genes.use = markers_plots)
```


## Set identities from marker gene expression

```{r}
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(0) ) , ident.use = "qNSC1"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(4) ) , ident.use = "qNSC2"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(6,8) ) , ident.use = "aNSC0"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(3,5) ) , ident.use = "aNSC1"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(1) ) , ident.use = "aNSC2"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(2) ) , ident.use = "TAP"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(7) ) , ident.use = "NB"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(10) ) , ident.use = "OPC"  )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(9) ) , ident.use = "OD"  )

```

### Build Cluster Tree

```{r}
seurat_10X2 <- BuildClusterTree(object = seurat_10X2 , pcs.use = 1:8)
```


### PCA Plot with identities

```{r}
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)

PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)

```

### tSNE feature plots: marker genes

```{r}

TSNEPlot(object = seurat_10X2)
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
```

### Violin plots: marker genes

```{r fig.width=6, fig.height=4}
celltypes_order <- c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD")


## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Active NSC markers
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## TAPs
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" ,  do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
```

```{r}
VlnPlot(object = seurat_10X2 , features.plot = c("nUMI")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("nGene")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("percent.mito")  , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
```


### Heatmap marker genes

```{r fig.height=12}
markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )

DoHeatmap(object = seurat_10X2 , genes.use = markers_plots , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE  )
```

```{r fig.height=12}
DoHeatmap(object = seurat_10X2 , genes.use = markers.custom.order , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
```

```{r fig.height=12}
DoHeatmap(object = seurat_10X2 , genes.use = markers , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE  )
```

## Save identities to meta.data table

```{r}
seurat_10X2 <- StashIdent(object = seurat_10X2 , save.name = "celltype") 
```


## Find marker genes for the subpopulations and save them as csv files

```{r}
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2)

# Split the table into one table for each cluster
markers_clusters <- list()

## Save up and down regulated genes per cluster
for( cl in unique(markers.seurat_10X2$cluster) ){
  markers_clusters[[cl]] <- list()
  markers_clusters[[cl]][["up"]] <- filter(markers.seurat_10X2 , cluster == cl & avg_logFC > 0 )
  markers_clusters[[cl]][["down"]] <- filter(markers.seurat_10X2 , cluster == cl & avg_logFC < 0 )
}

for( cl in unique(markers.seurat_10X2$cluster) ){
  for(j in c("up","down"))
  if(save_csv){ write.csv(x = markers_clusters[[cl]][[j]] , file = paste0("celltype_markers/celltype_" , cl , "_", j , "regulated" , "_markers_10X2_young_old.csv" )) }
}
```

## t-SNE plots with cluster identity or with age 

Color the tSNE plot by the age of the animals

```{r}
x <- FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("slateblue","yellowgreen") , no.legend = FALSE , do.return = TRUE )

tsne_ageplot <- ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal()

plotsize = 5

tsne_ageplot
#ggsave(plot = tsne_ageplot , filename = "age_tsne_hires.png" , width = plotsize , height = plotsize )
```

```{r fig.asp=1}
ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal() # + guides(color = "none")
```

Color the tSNE by the identified celltypes and subpopulations

```{r}
g <- TSNEPlot(seurat_10X2 , do.return = TRUE)

ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) + 
  geom_point(size = 1) + 
  labs( x = "tSNE_1" , y = "tSNE_2" ) + 
  coord_equal() + 
  scale_color_manual(values =   c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )
```

```{r fig.asp=1}
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) + 
  geom_point(size = 1) + 
  labs( x = "tSNE_1" , y = "tSNE_2" ) + 
  coord_equal() + 
  # guides(color = "none") + 
  scale_color_manual(values =   c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )

```


## Save the analysis results so far as RDS file
```{r}
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene_all_cells.RDS")
```

## Identify and filter out contaminating ependymal cells and leukocytes

We find that there are two small clusters in the tSNE plot that lie outside the qNSC2 and the aNSC0. Using the interactive visualisation feature we can determine the names of these cells and check for differentially expressed genes from those.

```{r}
outside_q2 <- c("GCGAGAATCGCTTAGA-1", "GTTAAGCGTGCACTTA-1", "TCATTTGTCGTCCAGG-1", 
"AACGTTGCACGCCAGT-2", "CAACTAGAGTCGCCGT-2", "CCACTACTCGGAATCT-2", 
"CCGTTCATCCTGCTTG-2", "CGCTGGAGTAGTACCT-2", "TACTTACTCCTAGGGC-2", 
"TAGTTGGCACATTAGC-2", "TGGCTGGCACTCAGGC-2", "TGTATTCCATGTTGAC-2"
)

outside_a0 <- c("ATGGGAGAGATCCGAG-1", "TACACGACAAAGTGCG-1", "CCATGTCTCCTAGGGC-2", 
"CGTGAGCCACCATGTA-2", "CTCGAAAAGTGCGATG-2", "TGACGGCCATCGATGT-2"
)
```

```{r}
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_a0 , ident.use = "outside_a0")
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_q2 , ident.use = "outside_q2")
```

```{r}
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
```

First the cells outside aNSC0

```{r}
outside_a0_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_a0")

outside_a0_markers
```

Checking the differentially expressed genes we can see many leukocyte marker genes, amongst them CD45.

```{r fig.width=15, fig.height=6}
## Ptprc = CD45 -> Leukocyte Common Antigen (LCA)

VlnPlot(object = seurat_10X2 ,  features.plot = c("Ptprc","Slc1a3") , x.lab.rot = TRUE )
```


Next the cells outside qNSC2

```{r}
outside_q2_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_q2")

outside_q2_markers
```

Checking the differentially expressed genes we can see ependymal cell genes (Genes from https://pdfs.semanticscholar.org/e833/5f8154f714088147d76701ee0052d3a388b5.pdf)

```{r fig.height=10, fig.width=20}
VlnPlot(object = seurat_10X2 ,  features.plot = c("Iqca","Bbox1","Apoe","Syne1","Meig1","Foxj1","Slc1a3") , x.lab.rot = TRUE )
```

Next we exclude these contaminating cells and save the new seurat objects as a RDS file.

```{r}
cells_to_remove <- WhichCells(object = seurat_10X2 , ident = c("outside_q2","outside_a0"))

cells_to_remove
```

After we made sure we know which kind of celltype these cells are, we can get rid of them in the following analysis

```{r}
all_cells <- WhichCells(object = seurat_10X2 )

cells_to_keep <- all_cells[! all_cells %in% cells_to_remove]

seurat_10X2 <- SubsetData(object = seurat_10X2 , cells.use = cells_to_keep )

TSNEPlot(object = seurat_10X2 , do.label = TRUE)
```

## Save the results as an RDS file

```{r}
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene.RDS")
```



SessionInfo

```{r}
sessionInfo()
```
